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Optimizing image Quality and Reliability Through Chunking Fusion and wavelet Transform

MAPAN-JOURNAL OF METROLOGY SOCIETY OF INDIA 2025年第1期40卷 43-58页

作者： Hu, Jiachong Jiangxi Univ Finance & Econ Sch Design & Art Nanchang 330032 Peoples R China

The study of visual communication and its role in conveying information through visual elements is crucial in aiding the comprehension of complex concepts. Computer imaging involves installing an operating system (OS), applications, and settings on new devices, with cloud-based methods streamlining the process and reducing overhead. As modern society becomes more reliant on digital technology, the complexity of information systems continues to grow, further emphasizing the importance of computer technology. This paper focuses on the integration of computer imaging technology and visual communication approaches. The proposed image processing system has been widely adopted and is now considered a key component of advanced, practical research. The importance of computer graphics in enhancing the quality and impact of visual communication has been fully acknowledged. Works produced using modern graphic image processing techniques have the potential to significantly influence viewers' perceptions. The study explores the impact of computer graphics and image technology on the development of visual communication, providing valuable data for innovative city applications. Additionally, the paper delves into the critical role of computer graphics and image technology in realizing various aspects of visual communication prototypes. For performance analysis, quality metrics such as Mean Square Error (MSE), Peak signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Contrast-to-Noise Ratio (CNR) are utilized, along with reliability metrics like Accuracy, Precision, Recall, and F1-Score. The proposed approach achieves superior efficiency compared to other methods. The performance results include MSE of 13%, PSNR of 40 dB, SSIM of 0.72, CNR of 39 dB, Accuracy of 98.76%, Precision of 98.64%, Recall of 97.56%, and an F1-Score of 97.87%.

关键词： Communication process Computer graphics Graphic design image processing Internet of things Communication design

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The Octonionic Moritoh Transform on Octonionic Function Spaces

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RUSSIAN MATHEMATICS 2025年第2期69卷 83-94页

作者： Kumar, A. Singh, S. K. Singh, Sh. K. Babasaheb Bhimrao Ambedkar Univ Lucknow 226025 India Mahatma Gandhi Cent Univ Motihari 845401 Bihar India

This article explores the challenges and applications of the octonion Fourier transform, with a focus on wavelet analysis. We extend the Moritoh wavelet transform to the octonionic Besov spaces, the weighted octonionic Besov spaces, the octonionic BMO spaces, and the octonionic weighted BMO spaces. The derived bounds for the octonionic Moritoh wavelet transform in these spaces contribute to a deeper understanding of its behavior. Our findings pave the way for future research in signal processing, image analysis, and the intersection of octonion wavelet analysis with other mathematical theories.

关键词： octonion Fourier transform wavelet transform Besov space BMO space

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Real-Time Multi-Scale Barcode image Deblurring Based on Edge Feature Guidance

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ELECTRONICS 2025年第7期14卷 1298-1298页

作者： Shi, Chenbo Jiang, Xin Zhang, Xiangyu Zhu, Changsheng Hu, Xiaowei Zhang, Guodong Li, Yuejia Zhang, Chun Shandong Univ Sci & Technol Coll Intelligent Equipment Tai An 271019 Peoples R China Suzhou Lamberv Intelligent Technol Dept Artificial Intelligence Suzhou 215000 Peoples R China

Barcode technology plays a crucial role in automatic identification and data acquisition systems, with extensive applications in retail, warehousing, healthcare, and industrial automation. However, barcode images often suffer from blurriness due to lighting conditions, camera quality, motion blur, and noise, adversely affecting their readability and system performance. This paper proposes a multi-scale real-time deblurring method based on edge feature guidance. Our designed multi-scale deblurring network integrates an edge feature fusion module (EFFM) to restore image edges better. Additionally, we introduce a feature filtering mechanism (FFM), which effectively suppresses noise interference by precisely filtering and enhancing critical signal features. Moreover, by incorporating wavelet reconstruction loss, the method significantly improves the restoration of details and textures. Extensive experiments on various barcode datasets demonstrate that our method significantly enhances barcode clarity and scanning accuracy, especially in noisy environments. Furthermore, our algorithm ensures robustness and real-time performance. The research results indicate that our method holds significant promise for enhancing barcode image processing, with potential applications in retail, logistics, inventory management, and industrial automation.

关键词： barcode deblurring multi-scale strategy neural networks edge guidance

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High-Quality Reconstruction of Depth Maps From Graph-Based Non-Uniform Sampling

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IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 780-791页

作者： Yang, Jingyu Xu, Wenqiang Hou, Yusen Ye, Xinchen Frossard, Pascal Li, Kun Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Dalian Univ Technol DUT RU Int Sch Informat Sci & Engn Dalian 116024 Peoples R China Ecole Polytech Fed Lausanne Signal Proc Lab CH-999034 Lausanne Switzerland Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China

Depth sensing is essential for intelligent computer vision applications, but it often suffers from low range precision and spatial resolution. To address this problem, we propose a novel framework that combines non-uniform sampling and reconstruction based on graph theory. Our framework consists of two main components: (1) a graph Laplacian induced non-uniform sampling (GLINUS) scheme that samples depth signals more densely around edges and contours than in smooth regions, and (2) an ensemble of priors (EoP) model that reconstructs the high-quality depth map using adaptive dual-tree discrete wavelet packets (ADDWP) transform, graph total variation regularizer, and graph Laplacian regularizer with color guidance. We solve the reconstruction problem using the alternating direction method of multipliers (ADMM). Our experiments demonstrate that our framework can capture fine structures and global information in depth signals and produce superior depth reconstruction results.

关键词： image reconstruction Laplace equations Spectral analysis Color Task analysis Filtering algorithms image color analysis Graph signal processing non-uniform sampling depth reconstruction

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viewport-Independent Blind Quality Assessment of AI-Generated Omnidirectional images via vision-Language Correspondence

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IEEE signal processing LETTERS 2025年 32卷 1630-1634页

作者： Liu, Xuelin Yan, Jiebin Lai, Chenyi Li, Yang Fang, Yuming Jiangxi Univ Finance & Econ Sch Comp & Artificial Intelligence Nanchang 330032 Peoples R China

The advancement of deep generation technology has significantly enhanced the growth of artificial intelligence-generated content (AIGC). Among these, AI-generated omnidirectional images (AGOIs), hold considerable promise for applications in virtual reality (VR). However, the quality of AGOIs varies widely, and there has been limited research focused on their quality assessment. In this letter, inspired by the characteristics of the human visual system, we propose a novel viewport-independent blindquality assessment method for AGOIs, termed vi-AGOIQA, which leverages vision-language correspondence. Specifically, to minimize the computational burden associated with viewport-based prediction methods for omnidirectional image quality assessment, a set of image patches are first extracted from AGOIs in Equirectangular Projection (ERP) format. Then, the correspondence between visual and textual inputs is effectively learned by utilizing the pre-trained image and text encoders of the Contrastive Language-image Pre-training (CLIP) model. Finally, a multimodal feature fusion module is applied to predict human visual preferences based on the learned knowledge of visual-language consistency. Extensive experiments conducted on publicly available database demonstrate the promising performance of the proposed method.

关键词： visualization Feature extraction Databases Quality assessment Predictive models Training Computational modeling Vectors Solid modeling image quality AI-generated omnidirectional image vision-language correspondence blind image quality assessment

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A Micro-Topography Enhancement Method for DEMs: Advancing Geological Hazard Identification

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REMOTE SENSING 2025年第5期17卷 920-920页

作者： He, Qiulin Dong, Xiujun Li, Haoliang Deng, Bo Sima, Jingsong Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Prot Chengdu 610059 Peoples R China

Geological hazards in densely vegetated mountainous regions are challenging to detect due to terrain concealment and the limitations of traditional visualization methods. This study introduces the LiDAR image highlighting algorithm (LIHA), a novel approach for enhancing micro-topographical features in digital elevation models (DEMs) derived from airborne LiDAR data. By analogizing terrain profiles to non-stationary spectral signals, LIHA applies locally estimated scatterplot smoothing (Loess smoothing), wavelet decomposition, and high-frequency component amplification to emphasize subtle features such as landslide boundaries, cracks, and gullies. The algorithm was validated using the Mengu landslide case study, where edge detection analysis revealed a 20-fold increase in identified micro-topographical features (from 1907 to 37,452) after enhancement. Quantitative evaluation demonstrated LIHA's effectiveness in improving both human interpretation and automated detection accuracy. The results highlight LIHA's potential to advance early geological hazard identification and mitigation, particularly when integrated with machine learning for future applications. This work bridges signal processing and geospatial analysis, offering a reproducible framework for high-precision terrain feature extraction in complex environments.

关键词： digital elevation model LiDAR micro-topographical features vision enhancement wavelet decomposition edge detection geological hazard identification

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Application of High-Definition Imaging Technology in Traffic violation Monitoring and Judicial Proceedings

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Traitement du signal 2025年第2期42卷 1007-1017页

作者： Hu, Zijun Li, Jia College of Humanities and Social Sciences Hebei Agricultural University Baoding071001 China

With the rapid growth of urbanization and the increasing number of motor vehicles, traffic violations have become more frequent, posing severe challenges to road safety and urban management. The application of high-definition (HD) imaging technology in traffic violation monitoring enables more accurate and efficient detection and processing of violations, significantly enhancing the intelligence level of traffic management and the effectiveness of judicial evidence collection. While existing research has made progress in developing and optimizing traffic violation monitoring systems, there remain significant shortcomings in multi-object tracking and the construction of judicial evidence chains. Current multi-object tracking algorithms are susceptible to occlusion and target loss in complex traffic environments, and existing judicial evidence chain construction methods heavily rely on single-image processing techniques, which limits the reliability and completeness of the evidence. This study focuses on two key aspects: (1) multi-object tracking in HD traffic violation monitoring images based on an improved DeepSORT algorithm and (2) judicial evidence chain construction using the Discrete wavelet Transform-Singular Value Decomposition (DWT-SVD) technique. The enhanced DeepSORT algorithm integrates deep learning with traditional tracking methods, significantly improving tracking accuracy and robustness in complex traffic conditions. Meanwhile, the DWT-SVD technique enables multi-level analysis and evidence extraction, strengthening the comprehensiveness and reliability of judicial evidence. This research not only advances the theoretical understanding of image processing in traffic violation monitoring and judicial applications but also provides valuable insights for practical implementation and broader adoption. ©2025 The authors.

关键词： image understanding

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Supervised single-channel dual domains speech enhancement technique using bidirectional long short-term memory

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Multimedia Tools and applications 2025年第5期84卷 2779-2803页

作者： Hosen, Md. Shakhawat Basir, Samiul Khan, Md. Farukuzzaman Asaduzzaman, A.O.M. Islam, Md. Mojahidul Islam, Md Shohidul Department of Computer Science and Engineering Islamic University Kushtia7003 Bangladesh Dhaka Bangladesh Hong Kong

This article introduces a brand new supervised method to make speech sound better and clearer by getting rid of background noise and mess-ups. It brings together two kinds of signal processes: the dual-tree complex wavelet transform (DTCWT) along with the short-time Fourier transform (STFT), in addition to a bidirectional long short-term memory (BLSTM) network. By taking the features from both time-frequency and wavelet domains, this way deals with issues of the discrete wavelet transform (DWT), like dealing with big changes and getting the direction right. The DTCWT makes subband signals that then change into a complex spectrogram using STFT. The size of this spectrogram is put into a bunch of layers in the BLSTM network, which finds speech bits well by knowing what came before and what’s coming next in data sequences. Soft mask parts are used to tweak this procedure. Matrix multiplication is implemented between the noisy complex spectrogram and the predicted mask of the model to produce the enhanced matrix. The final speech parts are made using inverse STFT and inverse DTCWT with the original parameters. How well this way works is tested against different kinds of noise, like stationary, quasi-stationary, and non-stationary, using both the IEEE Corpus and Aurora-2 databases. Results prove it gets a PESQ score of 3.34 and an STOI score of 0.918 at 0 signal-to-noise ratio (SNR) and also performs better for others. Other ways to gauge how good it is, like HASQI and HASPI, also do better than other methods, showing off better voice quality and understandability in different noisy spots. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： signal to noise ratio

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A Speech Enhancement Algorithm Combining wavelet Transform and Adaptive Filters 6

A Speech Enhancement Algorithm Combining Wavelet Transform a...

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6th IEEE International Conference on image processing, applications and Systems, IPAS 2025

作者： Shrestha, Aayam Ghorshi, Seyed Joorabchi, Marjan Panahi, Issa Firouzeh, Fereshteh Fakhar The University of Texas at Tyler Department of Electrical and Computer Engineering TX United States Sharif University of Technology School of Science and Engineering Iran The University of Texas at Dallas Department of Electrical and Computer Engineering TX United States

ISBN: (纸本)9798331506520

Noise as an unwanted interference can significantly degrade speech signals, especially those recorded by many microphones. This interference is modeled as additive noise that originates from a range of sources including White Gaussian Noise (WGN), babble, crowd, large city, and traffic noises. These disturbances can alter the characteristics of speech signals reducing both their quality and intelligibility. This paper introduces a novel approach designed to reduce noise and enhance the quality and intelligibility of speech signals. The proposed method combines wavelet Transform with Adaptive Filters, specifically the Wiener filter and RLS filter. The evaluation process involves testing noisy speech signals under realistic conditions with different signal-to-noise ratios (SNRs) and different types of additive noise. The objective measure is used for evaluation, including the perceptual evaluation of speech quality (PESQ). Results show that combining Wiener or RLS filtering with wavelet Transform significantly improves noise reduction, outperforming the use of wavelet Transform alone. © 2025 IEEE.

关键词： Adaptive filtering

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Multi-image encryption based on three-step phase-shifting digital holography

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JOURNAL OF OPTICS-INDIA 2025年 1-13页

作者： Li, Fei Fang, Hui Jing, Hao Su, Yonggang Hebei Univ Coll Elect & Informat Engn Baoding 071000 Hebei Peoples R China

As a cutting-edge imaging technology that combines optical holography principles with digital signal processing, digital holography holds significant potential for applications in optical image encryption. However, current optical image encryption methods based on digital holography are predominantly designed for single images. With the rapid development of big data, the Internet, and modern communication technologies, single-image encryption methods have increasingly struggled to meet the growing demands for image data privacy protection. Furthermore, the robustness of most existing optical image encryption methods has not been sufficiently validated through communication experiments. Therefore, this paper proposes a multi-image encryption scheme based on three-step phase-shifting digital holography. In this proposed scheme, a chaotic system is used to generate random phase mask keys during the encryption and decryption processes. This not only enhances the security of the encryption system but also simplifies the management and transmission of secret keys. By employing wavelet decomposition and reconstruction, this proposed scheme can simultaneously encrypt four grayscale images, thereby improving the efficiency of image encryption. Simulation results indicate that the proposed scheme offers high security, effectively resisting brute force attack. Additionally, orthogonal frequency division multiplexing (OFDM) communication experiments demonstrate that the proposed scheme is also highly robust.

关键词： Optical image encryption Three-step phase-shifting digital holography Chaotic random phase encoding OFDM communication system

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